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Enhancing Wrapper Based Algorithms for Selecting Optimal Features from Thyroid Disease Dataset
Open Access   Article

Enhancing Wrapper Based Algorithms for Selecting Optimal Features from Thyroid Disease Dataset
K. Pavya1 , B. Srinivasan2
1 Department of Computer Science, Vellalar College for Women, Bharathiar University, Tamilnadu, India.
2 Department of Computer Science, Gobi Arts and Science College, Bharathiar University, Tamilnadu, India.
Correspondence should be addressed to: pavyavcw@gmail.com.

Section:Research Paper, Product Type: Journal Paper
Volume-6 , Issue-3 , Page no. 7-13, Mar-2018

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v6i3.713

Online published on Mar 30, 2018

Copyright © K. Pavya, B. Srinivasan . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 
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Citation

IEEE Style Citation: K. Pavya, B. Srinivasan, “Enhancing Wrapper Based Algorithms for Selecting Optimal Features from Thyroid Disease Dataset”, International Journal of Computer Sciences and Engineering, Vol.6, Issue.3, pp.7-13, 2018.

MLA Style Citation: K. Pavya, B. Srinivasan "Enhancing Wrapper Based Algorithms for Selecting Optimal Features from Thyroid Disease Dataset." International Journal of Computer Sciences and Engineering 6.3 (2018): 7-13.

APA Style Citation: K. Pavya, B. Srinivasan, (2018). Enhancing Wrapper Based Algorithms for Selecting Optimal Features from Thyroid Disease Dataset. International Journal of Computer Sciences and Engineering, 6(3), 7-13.
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Abstract :
Advances in medical information technology have enabled healthcare industries to automatically collect huge amount of data through clinical laboratory examinations. Thyroid disease (TD) is a study of Endocrinology and is considered as one of the most common diseases that is frequently misunderstood and misdiagnosed. Machine learning techniques are increasingly introduced to construct the CAD systems owing to its strong capability of extracting complex relationships in the biomedical data. Feature selection is a technique to choose a subset of variables from the multidimensional data which can improve the classification accuracy in diversity datasets. In addition, the best feature subset selection method can reduce the cost of feature measurement. This work focuses on enhancing the wrapper based algorithms for feature selection.
Key-Words / Index Term :
Data Mining, Feature Selection, Wrapper Method, Genetic Algorithm, Ant Colony Optimization
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